Capturing "attrition intensifying" structural traits from didactic interaction sequences of MOOC learners

TL;DR

This paper introduces a graph-based method to analyze MOOC learner interaction sequences, uncovering structural traits that predict attrition and outperform ngram baselines in identifying engagement decline.

Contribution

It presents a novel graph-based approach combining video and forum data to extract indicators of learner engagement and predict attrition more effectively.

Findings

Graph-based features outperform ngram models in predicting attrition

Structural traits reflect persistence and regularity in learner interactions

Enhanced understanding of engagement decline in MOOCs

Abstract

This work is an attempt to discover hidden structural configurations in learning activity sequences of students in Massive Open Online Courses (MOOCs). Leveraging combined representations of video clickstream interactions and forum activities, we seek to fundamentally understand traits that are predictive of decreasing engagement over time. Grounded in the interdisciplinary field of network science, we follow a graph based approach to successfully extract indicators of active and passive MOOC participation that reflect persistence and regularity in the overall interaction footprint. Using these rich educational semantics, we focus on the problem of predicting student attrition, one of the major highlights of MOOC literature in the recent years. Our results indicate an improvement over a baseline ngram based approach in capturing "attrition intensifying" features from the learning activities that MOOC learners engage in. Implications for some compelling future research are discussed.